Design And Implementation Of Data Analysis System For Transformer Winding Dielectric Loss Test

With the continuous development of power industry and the increase of power grid capacity,the number of transformers is increasing.As the most important equipment in power transmission and transformation system,the safety and reliability of transformer operation is very important for power system and users.If a transformer is damaged due to serious defects during operation,it will not only produce high maintenance or replacement costs,but also cause serious consequences such as the decline of power grid stability and even partial regional power outage,resulting in greater economic losses.Therefore,judging the electrical performance of the transformer and evaluating the operation status of the transformer will play a very important role in the power industry Among them,transformer winding dielectric loss factor test is the most common and important test in transformer factory,handover,routine and diagnostic test.At present,the analysis method of transformer dielectric loss test data is mainly based on the comparison and analysis of the test data,historical test data and factory test data,which can not effectively test the reduction of local electrical insulation performance and predict the development trend of equipment defects.This thesis studies a set of analysis and prediction scheme of test data,establishes a prediction model of test data by using neural network,predicts the next test data and judges the equipment status of the next test by inputting the current test data and the historical data of the last test site,and obtains the change trend of test data through multiple prediction of test data Data trends can be more comprehensive analysis of equipment defects,and put forward targeted maintenance strategy,timely carry out equipment overhaul,to ensure that the equipment does not occur insulation failure in operation.In order to realize the prediction of test data,Back Propagation neural network is used in this thesis.With its strong ability to deal with nonlinear data,a large number of historical test data of transformer are collected.The first two test data are used as input group,and the third test data as output group.The simulation of test data change trend is completed.Finally,the dielectric loss test of transformer winding is successfully realized According to the prediction of test data,the simulation value of the neural network model is basically consistent with the actual measured value,and the test error is controlled within 2%.In the future research work,it can be applied to the analysis and prediction of test data,and further modeling on the basis of this thesis can realize the prediction of other test data.Matlab software is used in this thesis.after testing,the software realizes the functions of single group test data prediction,multi group test data batch import prediction,prediction data export,single group test data change trend chart drawing,transformer fault point warning and so on.